blocks|key|2238424|text|来自wiki|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2238425|2238426|数据仓库是组织以电子方式存储的数据的存储库，旨在促进报告和分析1。|blockquote|2238427|2238428|数据仓库的这个定义侧重于数据存储。但是，检索和分析数据、提取、转换和加载数据以及管理数据字典的方法也被认为是数据仓库系统的基本组件。许多对数据仓库的引用都使用了这一更广泛的上下文。因此，数据仓库的扩展定义包括商业智能工具、用于提取、转换和加载数据到存储库的工具，以及用于管理和检索元数据的工具。|2238429|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Data_warehouse|1^0|2|4|0|0|0|V|1|1|0|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|D|3|-4|5|6|7|X|8|@]|9|@]|C|$]]|$1|E|3|F|5|G|7|Y|8|@]|9|@$A|Z|B|10|1|11]]|C|$]]|$1|H|3|-4|5|6|7|12|8|@]|9|@]|C|$]]|$1|I|3|J|5|6|7|13|8|@]|9|@]|C|$]]|$1|K|3|-4|5|6|7|14|8|@]|9|@]|C|$]]]|L|$M|$5|N|O|P|C|$Q|R]]|S|$5|N|O|P|C|$Q|R]]]]

From <a href="http://en.wikipedia.org/wiki/Data_warehouse" rel="noreferrer">wiki</a>:

<blockquote>
 A data warehouse is a repository of an
 organization's electronically stored
 data, designed to facilitate reporting
 and analysis <a href="http://en.wikipedia.org/wiki/Data_warehouse" rel="noreferrer">1</a>.
 
 This definition of the data warehouse
 focuses on data storage. However, the
 means to retrieve and analyze data, to
 extract, transform and load data, and
 to manage the data dictionary are also
 considered essential components of a
 data warehousing system. Many
 references to data warehousing use
 this broader context. Thus, an
 expanded definition for data
 warehousing includes business
 intelligence tools, tools to extract,
 transform and load data into the
 repository, and tools to manage and
 retrieve metadata.
</blockquote>

blocks|key|2238452|text|数据仓库试图使不同的系统看起来像是同构的，而与底层技术或存储机制无关。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2238453|你可以进入数据仓库的“为什么”，但这是另一个问题。|2238454|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

A data warehouse is an attempt to make disparate systems appear to be homogeneous, regardless to the underlying technology or storage mechanism.

You could get into the 'why' of data warehousing, but thats a different question.

blocks|key|1384766|text|对于非技术人员来说，最好的描述是“存储在专门的计算机系统中的海量数据。数据通常与某个特定的领域相关，整个系统被设计为快速和针对某些特殊任务进行优化。存储在数据仓库中的数据主要用于分析或决策过程。”|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1384767|不确定这是否足够：)在互联网上有很多关于这个主题的引用，但是如果有人问我一个快速的定义，我会使用类似于我上面写的东西。|1384768|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

For non technical guys the best is to describe it as "Huge ammount of data stored in a specialized computer system. Data is usually related to some specific domain and whole system is designed to be fast and optimized for some special tasks. Data stored in data warehouses are mostly used for analysis or in decision making processes."

Not sure if this is enough:) There is a lot of references to this subject in the Internet but if somebody asked me for a quick definition I would use something similiar to that I wrote above.

blocks|key|638177|text|至少在理论上，数据仓库的想法是提供来自各种现有系统的数据的合并视图，通常认为重写这些系统来直接合并数据是不切实际的。因此，数据仓库从这些现有系统收集数据，并提供(至少是假象)所有数据都在一个地方，因此可以通过一种方式对其进行查询。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|638178|主要目的(通常)是允许来自现有系统的数据之间的关联。例如，您可以比较您的销售人员与客户X在一起的时间(存储在一个系统中)与客户X购买了多少时间(存储在第二个系统中)，以及客户X对他们收到的东西的满意度(当然，存储在第三个系统中)。|638179|从实际的角度来看，这通常意味着客户的想法在定义模糊和完全疯狂之间。成本和进度几乎是不可能猜测的，可靠的估计显然是不可能的。提供他真正想要的东西几乎肯定是不可能的，找出有用的东西需要足够的时间和工作，你的第一步是让你正在做的事情听起来足够专业，这样当他对成本和/或时间表有了一点了解时，他就不会心脏病发作。|638180|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

At least theoretically, the idea of a data warehouse is to provide a consolidated view of data from a variety of existing systems, which are generally considered impractical to rewrite to consolidate the data directly. Therefore, the data warehouse collects data from those existing systems, and provides (at least the illusion of) all the data being in one place, so it can be queried in one way.

The primary intent is (usually) to allow correlation between data from existing systems. For example you can compare how much time your sales person spent with customer X (that's stored in one system) to how much customer X bought (stored in a second system) and how happy customer X is with what they're receiving (stored, of course, in a third system).

From a practical viewpoint, it often means the customer's ideas are somewhere between poorly defined and completely insane. The cost and schedule are next to impossible to even guess at, and a solid estimate is clearly impossible. Delivering what he really wants is almost certainly impossible, and figuring out something that will be useful is going to take enough time and work, that your first step is to make what you're doing sound sufficiently technical that he won't have a heart attack when he gets an inkling of the cost and/or schedule.

blocks|key|1380760|text|从实践的角度来看:业务变化，环境变化，什么是昨天的重要问题，可能不是今天，也最有可能不是明天。在处理CEO级别的问题时，这一点尤其正确。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1380761|如果你不能预见问题会是什么，你唯一的选择就是提供快速回答任何问题的方法。这就是数据仓库试图或声称要做的事情。数据从哪里来，从多少个不同的系统中来，都是无关紧要的。|1380762|许多数据仓库在“快速回答任何问题”方面都失败了，因为他们的技术要求您将可用的原始数据“内翻”(制作立方体)以确保“快速”。定义这些立方体限制了可以回答的问题的种类。|1380763|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

From a practical viewpoint: businesses change, environments change, what was an important question yesterday, may not be today and most probably won't be tomorrow. This is especially true when dealing with questions at the CEO level. 

If you can't foresee what the questions will be, your only option is to provide the means to answer any question quickly. That's what data warehouses attempt or purport to do. Where the data comes from, and from how many disparate systems, is immaterial.

Many data warehouses fail in the "answer any question quickly" because their technology requires you to turn the available raw data "inside out" (making cubes) to ensure the "quickly". And defining those cubes restricts the variety of questions that can be answered.

blocks|key|1380777|text|数据仓库是一个数据库、数据加载和报告系统，旨在聚合来自多个源的数据，并以一种易于提取和报告的方式呈现这些数据。从实际的角度来看，一个成功的数据仓库项目的好处是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1380778|1380779|统计和财务报告-数据仓库使您可以轻松地处理汇总的数据并从中获得有用的分析，特别是在您有65537行或更多数据的情况下。|unordered-list-item|1380780|数据安全性-数据表现良好，不会为年轻玩家设置陷阱。没有经验的用户可以使用即席报告系统，并且在没有noticing.|1380781|Transparency的情况下报告中产生无效结果的风险很低-企业可以看到并识别底层系统中的数据问题。数据仓库可以是提高数据质量的好工具work.|1380782|Empowerment+-最终用户报告工具应该支持大多数管理信息需求，只需要由技术专家开发定制报告的最小集合。|1380783|1380784|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|P|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|Q|8|@]|9|@]|A|$]]|$1|F|3|G|5|E|7|R|8|@]|9|@]|A|$]]|$1|H|3|I|5|E|7|S|8|@]|9|@]|A|$]]|$1|J|3|K|5|E|7|T|8|@]|9|@]|A|$]]|$1|L|3|-4|5|6|7|U|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|V|8|@]|9|@]|A|$]]]|N|$]]

A data warehouse is a database, data load and reporting system designed to aggregate data from multiple sources and present it in a manner which is easy to extract and report on. From a practical standpoint, the benefits of a successful data warehouse project are:

<ul>
<li>Statistical and financial reporting - data warehouses make it easy to work with data in aggregate and get useful analysis from it, particularly where you have 65537 or more rows of data.</li>
<li>Data safety - the data is well behaved and doesn't have traps for young players. Ad-hoc reporting systems can be used by inexperienced users with a low risk of producing invalid results in the reports without noticing.</li>
<li>Transparency - the business can see and identify issues with data in the underlying systems. A data warehouse can be a good tool to drive data quality work.</li>
<li>Empowerment - end-user reporting tools should support the majority of management information requirements with only a minimal set requiring a bespoke report to be developed by a technical specialist.</li>
</ul>

blocks|key|636013|text|接吻。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|636014|数据仓库是与给定组织及其活动相关的数据存储库。这些数据将允许分析和报告各种相关维度的组织绩效，如时间、结构、活动流。这些维度可以组合在一起，并通过相关层次结构聚合结果。|636015|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

KISS...

A data warehouse is a repository of data related to a given organisation and its activities. This data will allow analysis and reporting of the performance of the organisation across various pertinent dimensions such as time, structure, streams of activity. These dimensions can be combined and results aggregated through relevant hierarchies.

blocks|key|2238685|text|简单来说..。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2238686|数据仓库是一种控制真实仓库中的项目的方法，它包含控制位置、库存、移动、报告、审计，以及“真实仓库”中有关“真实项目”的任何内容。|2238687|我希望它更简单。|2238688|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

In simple terms...

A data warehouse is a way to control the items in a real warehouse that contains control location, stock, movement, reporting, auditing, anything about "real items" at a "real warehouse".

I hope it's more simple.

blocks|key|3646442|text|据我所知，数据仓库只不过是一个用于查询和分析的关系型数据库。它通常包含从事务数据派生的历史数据。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3646443|根据William+Inmon的说法，数据仓库的定义是：|3646444|数据仓库是一个面向主题的、集成的、非易失性的和时变的数据集合，用于支持管理决策。|3646445|如果我们理性地思考，并且可以从here中理解，那么上述定义是合乎逻辑的和完美的|offset|length|3646446|entityMap|0|LINK|mutability|MUTABLE|url|http://www.code2learn.com/2012/09/what-is-data-warehouse.html^0|0|0|0|F|4|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|U|8|@]|9|@$H|V|I|W|1|X]]|A|$]]|$1|J|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]]]

From what I know Data warehouse is nothing but a relational database which is designed for query and analysis. It usually contains history data derived from transaction data.

As per William Inmon, Data Warehouse definition is :

Data Warehouse is a subject-oriented, integrated, nonvolatile and time-variant collection of data in support of management's decisions.

And the above definition is logical and perfect if we think rationally and can be understood from <a href="http://www.code2learn.com/2012/09/what-is-data-warehouse.html" rel="nofollow">here</a>

blocks|key|2238812|text|哇，我在做一些研究。这是我遇到的一个非常好的答案：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2238813|2238814|数据仓库...是针对查询、分析和报告而特别结构化和构建的数据事务的复制版。在非常简单的定义中，术语数据仓库是指系统地收集数据的过程，这些数据以有组织的方式存储，以便可以访问和检索这些数据，以便将来进行报告和文档分析|blockquote|2238815|2238816|2238817|出自理查德·马丁的"Data+Warehouse+100+Success+Secrets“|offset|length|2238818|entityMap|0|LINK|mutability|MUTABLE|url|http://books.google.tt/books?id=ENAxH8GmpacC&pg=PA143&lpg=PA143&dq=pros%2520and%2520cons%2520of%2520data%2520warehousing&source=bl&ots=yX_H8iVGFb&sig=hrPUldA0FUM7sWlp-2o7EGzCsI8&hl=en&sa=X&ei=epqBUaKeH4Oe8gTmlICgCQ&ved=0CF0Q6AEwBg#v=onepage&q&f=false^0|0|0|0|0|0|A|Y|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|U|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|V|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|W|8|@]|9|@]|A|$]]|$1|G|3|-4|5|6|7|X|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|Y|8|@]|9|@$J|Z|K|10|1|11]]|A|$]]|$1|L|3|-4|5|6|7|12|8|@]|9|@]|A|$]]]|M|$N|$5|O|P|Q|A|$R|S]]]]

Wow, I was doing some research. This is a really good answer I came across:

<blockquote>
 Data warehousing ... is the reproduced version of data transactions that are particularly structuralized and built for inquiry, analysis and reporting. In a very simple definition, the term data warehousing refers to the process of systematically collecting data that are stored in an organized manner so they can be accessed and retrieved for future reporting and document analysis
</blockquote>

It's from "<a href="http://books.google.tt/books?id=ENAxH8GmpacC&amp;pg=PA143&amp;lpg=PA143&amp;dq=pros%20and%20cons%20of%20data%20warehousing&amp;source=bl&amp;ots=yX_H8iVGFb&amp;sig=hrPUldA0FUM7sWlp-2o7EGzCsI8&amp;hl=en&amp;sa=X&amp;ei=epqBUaKeH4Oe8gTmlICgCQ&amp;ved=0CF0Q6AEwBg#v=onepage&amp;q&amp;f=false" rel="noreferrer">Data Warehouse 100 Success Secrets</a>" by Richard Martin

blocks|key|3646601|text|数据仓库是为查询和业务分析而非事务而设计的关系数据库，processing.It包含从事务数据派生的历史数据。业务分析师使用此历史数据来详细了解业务。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3646602|数据仓库应该具有以下特征：|3646603|面向|3646604|主题：|ordered-list-item|3646605|3646606|数据仓库有助于分析数据。例如，要了解一家公司的销售情况，数据仓库需要建立在销售数据上。使用这个数据仓库，我们可以找到去年的销售额。这种按主题(销售)定义数据仓库的能力使其成为面向主题的数据仓库。|3646607|集成的|3646608|：|3646609|3646610|从不同的来源获取数据，并将其转换为一致的格式。这包括解决度量单位、命名冲突等。|3646611|3646612|非易失性：|3646613|3646614|一旦数据进入数据仓库，数据就不应该被更新。|3646615|3646616|时间变量：|3646617|3646618|要分析业务，分析师需要大量数据。因此，数据仓库应该包含历史数据。|3646619|entityMap^0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|17|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|18|8|@]|9|@]|A|$]]|$1|F|3|G|5|H|7|19|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|1A|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|1B|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|1C|8|@]|9|@]|A|$]]|$1|N|3|O|5|H|7|1D|8|@]|9|@]|A|$]]|$1|P|3|-4|5|6|7|1E|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1F|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|1G|8|@]|9|@]|A|$]]|$1|T|3|U|5|H|7|1H|8|@]|9|@]|A|$]]|$1|V|3|-4|5|6|7|1I|8|@]|9|@]|A|$]]|$1|W|3|X|5|6|7|1J|8|@]|9|@]|A|$]]|$1|Y|3|-4|5|6|7|1K|8|@]|9|@]|A|$]]|$1|Z|3|10|5|H|7|1L|8|@]|9|@]|A|$]]|$1|11|3|-4|5|6|7|1M|8|@]|9|@]|A|$]]|$1|12|3|13|5|6|7|1N|8|@]|9|@]|A|$]]|$1|14|3|-4|5|6|7|1O|8|@]|9|@]|A|$]]]|15|$]]

A data warehouse is a relational database that is designed for query and business analysis rather than for transaction processing.It contains historical data derived from transaction data. This historical data is used by the business analysts to understand about the business in detail.

A data warehouse should have the following characteristics:

<ol>
<li>Subject oriented: </li>
</ol>

A data warehouse helps in analyzing the data. For example, to know about a company's sales, a data warehouse needs to build on sales data. Using this data warehouse we can find the last year sales. This ability to define a data warehouse by subject (sales) makes it a subject oriented.

<ol start="2">
<li>Integrated: </li>
</ol>

Bringing data from different sources and putting them in to a consistent format. This includes resolving the units of measures, naming conflicts etc.

<ol start="3">
<li>Non volatile: </li>
</ol>

Once the data enters into the data warehouse, the data should not be updated.

<ol start="4">
<li>Time variant: </li>
</ol>

To analyze the business, analysts need large amounts of data. So, the data warehouse should contain historical data.

blocks|key|3646711|text|数据仓库是一种综合技术，它为企业内的关键人员提供对企业内任何级别所需信息的访问。它是一个企业范围的框架，允许管理所有企业信息。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3646712|数据仓库是信息的中央存储库，可以对其进行分析以做出更明智的决策。数据通常以规律的节奏从事务系统、关系数据库和其他来源流入数据仓库。业务分析师、数据科学家和决策者通过业务智能(BI)工具、SQL客户端和其他分析应用程序访问数据。|3646713|​|3646714|📷|atomic|offset|length|3646715|3646716|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/ffaf659a2699f5fcdc4b9ec7407073b4.png|imageAlt^0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|V|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|A|$]]|$1|F|3|G|5|H|7|X|8|@]|9|@$I|Y|J|Z|1|10]]|A|$]]|$1|K|3|E|5|6|7|11|8|@]|9|@]|A|$]]|$1|L|3|-4|5|6|7|12|8|@]|9|@]|A|$]]]|M|$N|$5|O|P|Q|A|$R|S|T|-4]]]]

Data warehousing is a comprehensive technology that provides the key people within an enterprise with access to any level of required information within the enterprise. It is an enterprise wide framework that permits the management of all enterprise information.

A data warehouse is a central repository of information that can be analyzed to make better informed decisions. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence. Business analysts, data scientists, and decision makers access the data through business intelligence (BI) tools, SQL clients, and other analytics applications.

<a href="https://i.stack.imgur.com/XTBUB.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/XTBUB.png" alt="enter image description here"></a>

I was asked by a customer what the term "data warehouse" really means. 

I thought about ETL, details of the data model, differences to NoSQL, Clouds, 'normal' DBMS, MDM (Master Data Management) etc. but wasn't able to describe the term in a few words to him... (In fact I did some talking and left him un-illuminated.)

How can "data warehouse" described in 1-3 (or a bit more) sentences?

What is a data warehouse?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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一位客户问我“数据仓库”这个术语的真正含义是什么。我想过ETL，数据模型的细节，与NoSQL，云，‘普通’数据库管理系统，主数据管理等的区别，但无法用几句话向他描述这个术语……(事实上，我做了一些谈话，没有照亮他。)如何用1-3个(或更多)句子来描述“数据仓库”？

问什么是数据仓库？
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